Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.
tissue_of_interest = "Colon"
Load the requisite packages and some additional helper functions.
library(here)
library(useful)
library(Seurat)
library(dplyr)
library(Matrix)
save_dir = here('00_data_ingest', 'tissue_robj')
# read the metadata to get the plates we want
plate_metadata_filename = here('00_data_ingest', 'facs_raw_data', 'metadata_FACS.csv')
plate_metadata <- read.csv(plate_metadata_filename, sep=",", header = TRUE)
colnames(plate_metadata)[1] <- "plate.barcode"
plate_metadata
Subset the metadata on the tissue.
tissue_plates = filter(plate_metadata, tissue == tissue_of_interest)[,c('plate.barcode','tissue','subtissue','mouse.sex')]
tissue_plates
Load the read count data.
#Load the gene names and set the metadata columns by opening the first file
filename = here('00_data_ingest', 'facs_raw_data', 'FACS', paste0(tissue_of_interest, '-counts.csv'))
raw.data = read.csv(filename, sep=",", row.names=1)
# raw.data = data.frame(row.names = rownames(raw.data))
corner(raw.data)
Make a vector of plate barcodes for each cell
plate.barcodes = lapply(colnames(raw.data), function(x) strsplit(strsplit(x, "_")[[1]][1], '.', fixed=TRUE)[[1]][2])
head(plate.barcodes)
[[1]]
[1] "MAA001871"
[[2]]
[1] "MAA001871"
[[3]]
[1] "MAA001871"
[[4]]
[1] "MAA001871"
[[5]]
[1] "MAA001871"
[[6]]
[1] "MAA001871"
Use only the metadata rows corresponding to Bladder plates. Make a plate barcode dataframe to “expand” the per-plate metadata to be per-cell.
head(raw.data)
barcode.df = t.data.frame(as.data.frame(plate.barcodes))
rownames(barcode.df) = colnames(raw.data)
colnames(barcode.df) = c('plate.barcode')
head(barcode.df)
plate.barcode
F11.MAA001871.3_39_F.1.1 "MAA001871"
G16.MAA001871.3_39_F.1.1 "MAA001871"
I1.MAA001871.3_39_F.1.1 "MAA001871"
A13.MAA001871.3_39_F.1.1 "MAA001871"
B16.MAA001871.3_39_F.1.1 "MAA001871"
C18.MAA001871.3_39_F.1.1 "MAA001871"
rnames = row.names(barcode.df)
meta.data <- merge(barcode.df, plate_metadata, by='plate.barcode', sort = F)
row.names(meta.data) <- rnames
# Sort by plate barcode because that's how the data was originally
meta.data = meta.data[order(meta.data$plate.barcode), ]
raw.data = raw.data[, rownames(meta.data)]
head(raw.data)
Process the raw data and load it into the Seurat object.
# Find ERCC's, compute the percent ERCC, and drop them from the raw data.
erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]
# Create the Seurat object with all the data
tiss <- CreateSeuratObject(raw.data = raw.data, project = tissue_of_interest,
min.cells = 5, min.genes = 5)
tiss <- AddMetaData(object = tiss, meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")
# Change default name for sums of counts from nUMI to nReads
colnames(tiss@meta.data)[colnames(tiss@meta.data) == 'nUMI'] <- 'nReads'
# Create metadata columns for annotations and subannotations
tiss@meta.data[,'annotation'] <- NA
tiss@meta.data[,'subannotation'] <- NA
Calculate percent ribosomal genes.
ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@data), value = TRUE)
percent.ribo <- Matrix::colSums(tiss@raw.data[ribo.genes, ])/Matrix::colSums(tiss@raw.data)
tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")
A sanity check: genes per cell vs reads per cell.
GenePlot(object = tiss, gene1 = "nReads", gene2 = "nGene", use.raw=T)
Filter out cells with few reads and few genes.
tiss <- FilterCells(object = tiss, subset.names = c("nGene", "nReads"),
low.thresholds = c(500, 50000), high.thresholds = c(25000, 2000000))
Normalize the data, then regress out correlation with total reads
tiss <- NormalizeData(object = tiss)
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Run Principal Component Analysis.
tiss <- RunPCA(object = tiss, do.print = FALSE)
tiss <- ProjectPCA(object = tiss, do.print = FALSE)
Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.
PCElbowPlot(object = tiss)
Choose the number of principal components to use.
# Set number of principal components.
n.pcs = 20
The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.
For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.
# Set resolution
res.used <- 0.5
tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)
To visualize
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, perplexity=30)
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T)
Check expression of genes of interset.
#{r, echo=FALSE, fig.height=16, fig.width=12}
genes_to_check = c('Lgr5', 'Krt20', 'Atoh1', 'Chga', 'Dclk1', 'Mki67')
#genes_to_check = c('Alb', 'Cyp2f2', 'Cyp2e1', 'Hamp')
#FeaturePlot(tiss, genes_to_check, pt.size = 1, nCol = 3)
FeaturePlot(tiss, genes_to_check, pt.size = 1)
Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.
# To change the y-axis to show raw counts, add use.raw = T.
VlnPlot(tiss, genes_to_check)
# To change the y-axis to show raw counts, add use.raw = T.
VlnPlot(tiss, 'Lgr5')
# To get plots of two genes, add use.raw = T.
GenePlot(tiss, 'Krt20', 'Atoh1')
How big are the clusters?
table(tiss@ident)
0 1 2 3 4 5 6 7 8 9 10 11 12
988 664 583 231 220 220 200 153 143 133 132 55 25
Which markers identify a specific cluster?
clust.markers <- FindMarkers(object = tiss, ident.1 = 0, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
| | 0 % ~calculating
|+ | 1 % ~02m 44s
|+ | 2 % ~02m 36s
|++ | 3 % ~02m 33s
|++ | 4 % ~02m 32s
|+++ | 5 % ~02m 30s
|+++ | 6 % ~02m 30s
|++++ | 7 % ~02m 27s
|++++ | 8 % ~02m 25s
|+++++ | 9 % ~02m 22s
|+++++ | 10% ~02m 21s
|++++++ | 11% ~02m 19s
|++++++ | 12% ~02m 18s
|+++++++ | 13% ~02m 16s
|+++++++ | 14% ~02m 14s
|++++++++ | 15% ~02m 13s
|++++++++ | 16% ~02m 11s
|+++++++++ | 17% ~02m 09s
|+++++++++ | 18% ~02m 08s
|++++++++++ | 19% ~02m 06s
|++++++++++ | 20% ~02m 08s
|+++++++++++ | 21% ~02m 06s
|+++++++++++ | 22% ~02m 04s
|++++++++++++ | 23% ~02m 02s
|++++++++++++ | 24% ~02m 01s
|+++++++++++++ | 25% ~02m 01s
|+++++++++++++ | 26% ~01m 59s
|++++++++++++++ | 27% ~01m 57s
|++++++++++++++ | 28% ~01m 55s
|+++++++++++++++ | 29% ~01m 53s
|+++++++++++++++ | 30% ~01m 52s
|++++++++++++++++ | 31% ~01m 50s
|++++++++++++++++ | 32% ~01m 48s
|+++++++++++++++++ | 33% ~01m 47s
|+++++++++++++++++ | 34% ~01m 46s
|++++++++++++++++++ | 35% ~01m 45s
|++++++++++++++++++ | 36% ~01m 44s
|+++++++++++++++++++ | 37% ~01m 42s
|+++++++++++++++++++ | 38% ~01m 41s
|++++++++++++++++++++ | 39% ~01m 39s
|++++++++++++++++++++ | 40% ~01m 37s
|+++++++++++++++++++++ | 41% ~01m 36s
|+++++++++++++++++++++ | 42% ~01m 34s
|++++++++++++++++++++++ | 43% ~01m 32s
|++++++++++++++++++++++ | 44% ~01m 31s
|+++++++++++++++++++++++ | 45% ~01m 29s
|+++++++++++++++++++++++ | 46% ~01m 27s
|++++++++++++++++++++++++ | 47% ~01m 26s
|++++++++++++++++++++++++ | 48% ~01m 24s
|+++++++++++++++++++++++++ | 49% ~01m 22s
|+++++++++++++++++++++++++ | 50% ~01m 21s
|++++++++++++++++++++++++++ | 51% ~01m 19s
|++++++++++++++++++++++++++ | 52% ~01m 17s
|+++++++++++++++++++++++++++ | 53% ~01m 16s
|+++++++++++++++++++++++++++ | 54% ~01m 14s
|++++++++++++++++++++++++++++ | 55% ~01m 12s
|++++++++++++++++++++++++++++ | 56% ~01m 11s
|+++++++++++++++++++++++++++++ | 57% ~01m 09s
|+++++++++++++++++++++++++++++ | 58% ~01m 08s
|++++++++++++++++++++++++++++++ | 59% ~01m 06s
|++++++++++++++++++++++++++++++ | 60% ~01m 04s
|+++++++++++++++++++++++++++++++ | 61% ~01m 03s
|+++++++++++++++++++++++++++++++ | 62% ~01m 01s
|++++++++++++++++++++++++++++++++ | 63% ~59s
|++++++++++++++++++++++++++++++++ | 64% ~58s
|+++++++++++++++++++++++++++++++++ | 65% ~56s
|+++++++++++++++++++++++++++++++++ | 66% ~54s
|++++++++++++++++++++++++++++++++++ | 67% ~53s
|++++++++++++++++++++++++++++++++++ | 68% ~51s
|+++++++++++++++++++++++++++++++++++ | 69% ~50s
|+++++++++++++++++++++++++++++++++++ | 70% ~48s
|++++++++++++++++++++++++++++++++++++ | 71% ~46s
|++++++++++++++++++++++++++++++++++++ | 72% ~45s
|+++++++++++++++++++++++++++++++++++++ | 73% ~43s
|+++++++++++++++++++++++++++++++++++++ | 74% ~42s
|++++++++++++++++++++++++++++++++++++++ | 75% ~40s
|++++++++++++++++++++++++++++++++++++++ | 76% ~38s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~37s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~35s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~34s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~32s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~30s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~29s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~27s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~26s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~24s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~22s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~21s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~19s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~18s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~16s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~14s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 39s
print(x = head(x= clust.markers, n = 10))
You can also compute all markers for all clusters at once. This may take some time.
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
| | 0 % ~calculating
|+ | 1 % ~02m 23s
|++ | 2 % ~03m 10s
|++ | 3 % ~02m 54s
|+++ | 4 % ~02m 46s
|+++ | 5 % ~02m 40s
|++++ | 6 % ~02m 36s
|++++ | 7 % ~02m 33s
|+++++ | 8 % ~02m 31s
|+++++ | 9 % ~02m 28s
|++++++ | 10% ~02m 26s
|++++++ | 11% ~02m 24s
|+++++++ | 12% ~02m 22s
|+++++++ | 13% ~02m 20s
|++++++++ | 14% ~02m 18s
|++++++++ | 15% ~02m 15s
|+++++++++ | 16% ~02m 14s
|+++++++++ | 17% ~02m 12s
|++++++++++ | 18% ~02m 15s
|++++++++++ | 19% ~02m 13s
|+++++++++++ | 20% ~02m 11s
|+++++++++++ | 21% ~02m 09s
|++++++++++++ | 22% ~02m 07s
|++++++++++++ | 23% ~02m 05s
|+++++++++++++ | 24% ~02m 03s
|+++++++++++++ | 26% ~02m 01s
|++++++++++++++ | 27% ~01m 59s
|++++++++++++++ | 28% ~01m 58s
|+++++++++++++++ | 29% ~01m 56s
|+++++++++++++++ | 30% ~01m 54s
|++++++++++++++++ | 31% ~01m 53s
|++++++++++++++++ | 32% ~01m 51s
|+++++++++++++++++ | 33% ~01m 52s
|+++++++++++++++++ | 34% ~01m 50s
|++++++++++++++++++ | 35% ~01m 48s
|++++++++++++++++++ | 36% ~01m 48s
|+++++++++++++++++++ | 37% ~01m 46s
|+++++++++++++++++++ | 38% ~01m 44s
|++++++++++++++++++++ | 39% ~01m 42s
|++++++++++++++++++++ | 40% ~01m 41s
|+++++++++++++++++++++ | 41% ~01m 39s
|+++++++++++++++++++++ | 42% ~01m 37s
|++++++++++++++++++++++ | 43% ~01m 36s
|++++++++++++++++++++++ | 44% ~01m 34s
|+++++++++++++++++++++++ | 45% ~01m 33s
|+++++++++++++++++++++++ | 46% ~01m 31s
|++++++++++++++++++++++++ | 47% ~01m 29s
|++++++++++++++++++++++++ | 48% ~01m 27s
|+++++++++++++++++++++++++ | 49% ~01m 26s
|+++++++++++++++++++++++++ | 50% ~01m 24s
|++++++++++++++++++++++++++ | 51% ~01m 23s
|+++++++++++++++++++++++++++ | 52% ~01m 21s
|+++++++++++++++++++++++++++ | 53% ~01m 19s
|++++++++++++++++++++++++++++ | 54% ~01m 18s
|++++++++++++++++++++++++++++ | 55% ~01m 16s
|+++++++++++++++++++++++++++++ | 56% ~01m 14s
|+++++++++++++++++++++++++++++ | 57% ~01m 12s
|++++++++++++++++++++++++++++++ | 58% ~01m 10s
|++++++++++++++++++++++++++++++ | 59% ~01m 08s
|+++++++++++++++++++++++++++++++ | 60% ~01m 07s
|+++++++++++++++++++++++++++++++ | 61% ~01m 05s
|++++++++++++++++++++++++++++++++ | 62% ~01m 04s
|++++++++++++++++++++++++++++++++ | 63% ~01m 02s
|+++++++++++++++++++++++++++++++++ | 64% ~01m 00s
|+++++++++++++++++++++++++++++++++ | 65% ~58s
|++++++++++++++++++++++++++++++++++ | 66% ~56s
|++++++++++++++++++++++++++++++++++ | 67% ~55s
|+++++++++++++++++++++++++++++++++++ | 68% ~53s
|+++++++++++++++++++++++++++++++++++ | 69% ~51s
|++++++++++++++++++++++++++++++++++++ | 70% ~49s
|++++++++++++++++++++++++++++++++++++ | 71% ~48s
|+++++++++++++++++++++++++++++++++++++ | 72% ~46s
|+++++++++++++++++++++++++++++++++++++ | 73% ~44s
|++++++++++++++++++++++++++++++++++++++ | 74% ~42s
|++++++++++++++++++++++++++++++++++++++ | 76% ~41s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~39s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~37s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~36s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~34s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~32s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~31s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~29s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~27s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~25s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~24s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~22s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~20s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~19s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~17s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~15s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~14s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 45s
| | 0 % ~calculating
|+ | 1 % ~01m 39s
|++ | 2 % ~01m 42s
|++ | 3 % ~01m 38s
|+++ | 4 % ~01m 37s
|+++ | 5 % ~01m 35s
|++++ | 6 % ~01m 45s
|++++ | 7 % ~01m 43s
|+++++ | 8 % ~01m 40s
|+++++ | 9 % ~01m 38s
|++++++ | 10% ~01m 37s
|++++++ | 11% ~01m 35s
|+++++++ | 12% ~01m 38s
|+++++++ | 14% ~01m 36s
|++++++++ | 15% ~01m 34s
|++++++++ | 16% ~01m 33s
|+++++++++ | 17% ~01m 31s
|+++++++++ | 18% ~01m 30s
|++++++++++ | 19% ~01m 31s
|++++++++++ | 20% ~01m 30s
|+++++++++++ | 21% ~01m 28s
|+++++++++++ | 22% ~01m 27s
|++++++++++++ | 23% ~01m 25s
|++++++++++++ | 24% ~01m 24s
|+++++++++++++ | 25% ~01m 22s
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|++++++++++++++ | 27% ~01m 19s
|+++++++++++++++ | 28% ~01m 18s
|+++++++++++++++ | 29% ~01m 16s
|++++++++++++++++ | 30% ~01m 15s
|++++++++++++++++ | 31% ~01m 15s
|+++++++++++++++++ | 32% ~01m 14s
|+++++++++++++++++ | 33% ~01m 13s
|++++++++++++++++++ | 34% ~01m 11s
|++++++++++++++++++ | 35% ~01m 10s
|+++++++++++++++++++ | 36% ~01m 09s
|+++++++++++++++++++ | 38% ~01m 08s
|++++++++++++++++++++ | 39% ~01m 06s
|++++++++++++++++++++ | 40% ~01m 05s
|+++++++++++++++++++++ | 41% ~01m 04s
|+++++++++++++++++++++ | 42% ~01m 03s
|++++++++++++++++++++++ | 43% ~01m 01s
|++++++++++++++++++++++ | 44% ~01m 00s
|+++++++++++++++++++++++ | 45% ~59s
|+++++++++++++++++++++++ | 46% ~59s
|++++++++++++++++++++++++ | 47% ~57s
|++++++++++++++++++++++++ | 48% ~56s
|+++++++++++++++++++++++++ | 49% ~55s
|+++++++++++++++++++++++++ | 50% ~54s
|++++++++++++++++++++++++++ | 51% ~53s
|+++++++++++++++++++++++++++ | 52% ~51s
|+++++++++++++++++++++++++++ | 53% ~50s
|++++++++++++++++++++++++++++ | 54% ~49s
|++++++++++++++++++++++++++++ | 55% ~48s
|+++++++++++++++++++++++++++++ | 56% ~47s
|+++++++++++++++++++++++++++++ | 57% ~46s
|++++++++++++++++++++++++++++++ | 58% ~45s
|++++++++++++++++++++++++++++++ | 59% ~43s
|+++++++++++++++++++++++++++++++ | 60% ~42s
|+++++++++++++++++++++++++++++++ | 61% ~41s
|++++++++++++++++++++++++++++++++ | 62% ~40s
|++++++++++++++++++++++++++++++++ | 64% ~39s
|+++++++++++++++++++++++++++++++++ | 65% ~38s
|+++++++++++++++++++++++++++++++++ | 66% ~36s
|++++++++++++++++++++++++++++++++++ | 67% ~35s
|++++++++++++++++++++++++++++++++++ | 68% ~34s
|+++++++++++++++++++++++++++++++++++ | 69% ~33s
|+++++++++++++++++++++++++++++++++++ | 70% ~32s
|++++++++++++++++++++++++++++++++++++ | 71% ~31s
|++++++++++++++++++++++++++++++++++++ | 72% ~29s
|+++++++++++++++++++++++++++++++++++++ | 73% ~28s
|+++++++++++++++++++++++++++++++++++++ | 74% ~27s
|++++++++++++++++++++++++++++++++++++++ | 75% ~26s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~25s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~24s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~23s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~22s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~21s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~20s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~19s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~18s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~16s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 46s
| | 0 % ~calculating
|+ | 1 % ~02m 01s
|+ | 2 % ~01m 59s
|++ | 3 % ~01m 59s
|++ | 4 % ~02m 01s
|+++ | 5 % ~01m 59s
|+++ | 6 % ~01m 58s
|++++ | 7 % ~01m 57s
|++++ | 8 % ~01m 55s
|+++++ | 9 % ~01m 54s
|+++++ | 10% ~01m 52s
|++++++ | 11% ~01m 50s
|++++++ | 12% ~01m 49s
|+++++++ | 13% ~01m 48s
|+++++++ | 14% ~01m 46s
|++++++++ | 15% ~01m 45s
|++++++++ | 16% ~01m 44s
|+++++++++ | 17% ~01m 43s
|+++++++++ | 18% ~01m 42s
|++++++++++ | 19% ~01m 43s
|++++++++++ | 20% ~01m 43s
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|++++++++++++ | 23% ~01m 40s
|++++++++++++ | 24% ~01m 38s
|+++++++++++++ | 25% ~01m 37s
|+++++++++++++ | 26% ~01m 36s
|++++++++++++++ | 27% ~01m 34s
|++++++++++++++ | 28% ~01m 33s
|+++++++++++++++ | 29% ~01m 32s
|+++++++++++++++ | 30% ~01m 30s
|++++++++++++++++ | 31% ~01m 30s
|++++++++++++++++ | 32% ~01m 29s
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|++++++++++++++++++++++++ | 47% ~01m 09s
|++++++++++++++++++++++++ | 48% ~01m 08s
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|+++++++++++++++++++++++++ | 50% ~01m 05s
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|+++++++++++++++++++++++++++ | 53% ~01m 01s
|+++++++++++++++++++++++++++ | 54% ~60s
|++++++++++++++++++++++++++++ | 55% ~58s
|++++++++++++++++++++++++++++ | 56% ~57s
|+++++++++++++++++++++++++++++ | 57% ~56s
|+++++++++++++++++++++++++++++ | 58% ~55s
|++++++++++++++++++++++++++++++ | 59% ~53s
|++++++++++++++++++++++++++++++ | 60% ~52s
|+++++++++++++++++++++++++++++++ | 61% ~51s
|+++++++++++++++++++++++++++++++ | 62% ~49s
|++++++++++++++++++++++++++++++++ | 63% ~48s
|++++++++++++++++++++++++++++++++ | 64% ~47s
|+++++++++++++++++++++++++++++++++ | 65% ~45s
|+++++++++++++++++++++++++++++++++ | 66% ~44s
|++++++++++++++++++++++++++++++++++ | 67% ~43s
|++++++++++++++++++++++++++++++++++ | 68% ~42s
|+++++++++++++++++++++++++++++++++++ | 69% ~40s
|+++++++++++++++++++++++++++++++++++ | 70% ~39s
|++++++++++++++++++++++++++++++++++++ | 71% ~38s
|++++++++++++++++++++++++++++++++++++ | 72% ~36s
|+++++++++++++++++++++++++++++++++++++ | 73% ~35s
|+++++++++++++++++++++++++++++++++++++ | 74% ~34s
|++++++++++++++++++++++++++++++++++++++ | 75% ~33s
|++++++++++++++++++++++++++++++++++++++ | 76% ~31s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~30s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~29s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~27s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~26s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~25s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~23s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~22s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~21s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~19s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~18s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~17s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~16s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~14s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~13s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~12s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 09s
| | 0 % ~calculating
|+ | 1 % ~58s
|++ | 2 % ~56s
|++ | 3 % ~56s
|+++ | 4 % ~57s
|+++ | 5 % ~57s
|++++ | 6 % ~57s
|++++ | 7 % ~57s
|+++++ | 8 % ~57s
|+++++ | 9 % ~56s
|++++++ | 11% ~56s
|++++++ | 12% ~55s
|+++++++ | 13% ~55s
|+++++++ | 14% ~54s
|++++++++ | 15% ~53s
|++++++++ | 16% ~52s
|+++++++++ | 17% ~51s
|+++++++++ | 18% ~51s
|++++++++++ | 19% ~50s
|++++++++++ | 20% ~49s
|+++++++++++ | 21% ~48s
|++++++++++++ | 22% ~48s
|++++++++++++ | 23% ~48s
|+++++++++++++ | 24% ~47s
|+++++++++++++ | 25% ~46s
|++++++++++++++ | 26% ~46s
|++++++++++++++ | 27% ~45s
|+++++++++++++++ | 28% ~44s
|+++++++++++++++ | 29% ~43s
|++++++++++++++++ | 31% ~43s
|++++++++++++++++ | 32% ~42s
|+++++++++++++++++ | 33% ~42s
|+++++++++++++++++ | 34% ~41s
|++++++++++++++++++ | 35% ~40s
|++++++++++++++++++ | 36% ~40s
|+++++++++++++++++++ | 37% ~39s
|+++++++++++++++++++ | 38% ~38s
|++++++++++++++++++++ | 39% ~38s
|++++++++++++++++++++ | 40% ~37s
|+++++++++++++++++++++ | 41% ~36s
|++++++++++++++++++++++ | 42% ~36s
|++++++++++++++++++++++ | 43% ~35s
|+++++++++++++++++++++++ | 44% ~34s
|+++++++++++++++++++++++ | 45% ~34s
|++++++++++++++++++++++++ | 46% ~33s
|++++++++++++++++++++++++ | 47% ~32s
|+++++++++++++++++++++++++ | 48% ~32s
|+++++++++++++++++++++++++ | 49% ~31s
|++++++++++++++++++++++++++ | 51% ~30s
|++++++++++++++++++++++++++ | 52% ~30s
|+++++++++++++++++++++++++++ | 53% ~29s
|+++++++++++++++++++++++++++ | 54% ~28s
|++++++++++++++++++++++++++++ | 55% ~28s
|++++++++++++++++++++++++++++ | 56% ~27s
|+++++++++++++++++++++++++++++ | 57% ~27s
|+++++++++++++++++++++++++++++ | 58% ~26s
|++++++++++++++++++++++++++++++ | 59% ~25s
|++++++++++++++++++++++++++++++ | 60% ~25s
|+++++++++++++++++++++++++++++++ | 61% ~24s
|++++++++++++++++++++++++++++++++ | 62% ~23s
|++++++++++++++++++++++++++++++++ | 63% ~23s
|+++++++++++++++++++++++++++++++++ | 64% ~22s
|+++++++++++++++++++++++++++++++++ | 65% ~22s
|++++++++++++++++++++++++++++++++++ | 66% ~21s
|++++++++++++++++++++++++++++++++++ | 67% ~20s
|+++++++++++++++++++++++++++++++++++ | 68% ~20s
|+++++++++++++++++++++++++++++++++++ | 69% ~19s
|++++++++++++++++++++++++++++++++++++ | 71% ~18s
|++++++++++++++++++++++++++++++++++++ | 72% ~18s
|+++++++++++++++++++++++++++++++++++++ | 73% ~17s
|+++++++++++++++++++++++++++++++++++++ | 74% ~16s
|++++++++++++++++++++++++++++++++++++++ | 75% ~16s
|++++++++++++++++++++++++++++++++++++++ | 76% ~15s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~14s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~14s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~13s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~12s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~12s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~11s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~10s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 03s
| | 0 % ~calculating
|+ | 1 % ~01m 46s
|++ | 2 % ~01m 48s
|++ | 3 % ~01m 47s
|+++ | 4 % ~01m 46s
|+++ | 5 % ~01m 45s
|++++ | 6 % ~01m 43s
|++++ | 7 % ~01m 42s
|+++++ | 8 % ~01m 40s
|+++++ | 9 % ~01m 39s
|++++++ | 10% ~01m 38s
|++++++ | 11% ~01m 37s
|+++++++ | 12% ~01m 37s
|+++++++ | 13% ~01m 36s
|++++++++ | 14% ~01m 35s
|++++++++ | 15% ~01m 33s
|+++++++++ | 16% ~01m 32s
|+++++++++ | 17% ~01m 31s
|++++++++++ | 18% ~01m 30s
|++++++++++ | 19% ~01m 29s
|+++++++++++ | 20% ~01m 28s
|+++++++++++ | 21% ~01m 26s
|++++++++++++ | 22% ~01m 25s
|++++++++++++ | 23% ~01m 24s
|+++++++++++++ | 24% ~01m 23s
|+++++++++++++ | 25% ~01m 22s
|++++++++++++++ | 26% ~01m 21s
|++++++++++++++ | 27% ~01m 20s
|+++++++++++++++ | 28% ~01m 19s
|+++++++++++++++ | 29% ~01m 17s
|++++++++++++++++ | 30% ~01m 16s
|++++++++++++++++ | 31% ~01m 15s
|+++++++++++++++++ | 32% ~01m 14s
|+++++++++++++++++ | 33% ~01m 13s
|++++++++++++++++++ | 34% ~01m 12s
|++++++++++++++++++ | 35% ~01m 11s
|+++++++++++++++++++ | 36% ~01m 09s
|+++++++++++++++++++ | 37% ~01m 08s
|++++++++++++++++++++ | 38% ~01m 07s
|++++++++++++++++++++ | 39% ~01m 06s
|+++++++++++++++++++++ | 40% ~01m 05s
|+++++++++++++++++++++ | 41% ~01m 04s
|++++++++++++++++++++++ | 42% ~01m 03s
|++++++++++++++++++++++ | 43% ~01m 02s
|+++++++++++++++++++++++ | 44% ~01m 00s
|+++++++++++++++++++++++ | 45% ~59s
|++++++++++++++++++++++++ | 46% ~58s
|++++++++++++++++++++++++ | 47% ~57s
|+++++++++++++++++++++++++ | 48% ~56s
|+++++++++++++++++++++++++ | 49% ~55s
|++++++++++++++++++++++++++ | 51% ~54s
|++++++++++++++++++++++++++ | 52% ~53s
|+++++++++++++++++++++++++++ | 53% ~52s
|+++++++++++++++++++++++++++ | 54% ~51s
|++++++++++++++++++++++++++++ | 55% ~49s
|++++++++++++++++++++++++++++ | 56% ~48s
|+++++++++++++++++++++++++++++ | 57% ~47s
|+++++++++++++++++++++++++++++ | 58% ~46s
|++++++++++++++++++++++++++++++ | 59% ~45s
|++++++++++++++++++++++++++++++ | 60% ~44s
|+++++++++++++++++++++++++++++++ | 61% ~43s
|+++++++++++++++++++++++++++++++ | 62% ~42s
|++++++++++++++++++++++++++++++++ | 63% ~41s
|++++++++++++++++++++++++++++++++ | 64% ~40s
|+++++++++++++++++++++++++++++++++ | 65% ~39s
|+++++++++++++++++++++++++++++++++ | 66% ~37s
|++++++++++++++++++++++++++++++++++ | 67% ~36s
|++++++++++++++++++++++++++++++++++ | 68% ~35s
|+++++++++++++++++++++++++++++++++++ | 69% ~34s
|+++++++++++++++++++++++++++++++++++ | 70% ~33s
|++++++++++++++++++++++++++++++++++++ | 71% ~32s
|++++++++++++++++++++++++++++++++++++ | 72% ~31s
|+++++++++++++++++++++++++++++++++++++ | 73% ~30s
|+++++++++++++++++++++++++++++++++++++ | 74% ~29s
|++++++++++++++++++++++++++++++++++++++ | 75% ~27s
|++++++++++++++++++++++++++++++++++++++ | 76% ~26s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~25s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~24s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~23s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~22s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~21s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~20s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~19s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~18s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~17s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~15s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 50s
| | 0 % ~calculating
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+ | 1 % ~02m 02s
|++ | 2 % ~01m 49s
|++ | 3 % ~01m 49s
|+++ | 4 % ~01m 45s
|+++ | 5 % ~01m 42s
|++++ | 6 % ~01m 39s
|++++ | 7 % ~01m 36s
|+++++ | 8 % ~01m 35s
|+++++ | 9 % ~01m 34s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++ | 11% ~01m 33s
|++++++ | 12% ~01m 31s
|+++++++ | 13% ~01m 28s
|+++++++ | 14% ~01m 27s
|++++++++ | 15% ~01m 25s
|++++++++ | 16% ~01m 24s
|+++++++++ | 17% ~01m 22s
|+++++++++ | 18% ~01m 21s
|++++++++++ | 19% ~01m 19s
|++++++++++ | 20% ~01m 17s
|+++++++++++ | 21% ~01m 16s
|++++++++++++ | 22% ~01m 14s
|++++++++++++ | 23% ~01m 12s
|+++++++++++++ | 24% ~01m 11s
|+++++++++++++ | 25% ~01m 09s
|++++++++++++++ | 26% ~01m 08s
|++++++++++++++ | 27% ~01m 07s
|+++++++++++++++ | 28% ~01m 05s
|+++++++++++++++ | 29% ~01m 04s
|++++++++++++++++ | 31% ~01m 03s
|++++++++++++++++ | 32% ~01m 02s
|+++++++++++++++++ | 33% ~01m 01s
|+++++++++++++++++ | 34% ~01m 00s
|++++++++++++++++++ | 35% ~59s
|++++++++++++++++++ | 36% ~58s
|+++++++++++++++++++ | 37% ~57s
|+++++++++++++++++++ | 38% ~56s
|++++++++++++++++++++ | 39% ~55s
|++++++++++++++++++++ | 40% ~54s
|+++++++++++++++++++++ | 41% ~53s
|++++++++++++++++++++++ | 42% ~52s
|++++++++++++++++++++++ | 43% ~51s
|+++++++++++++++++++++++ | 44% ~50s
|+++++++++++++++++++++++ | 45% ~49s
|++++++++++++++++++++++++ | 46% ~48s
|++++++++++++++++++++++++ | 47% ~47s
|+++++++++++++++++++++++++ | 48% ~46s
|+++++++++++++++++++++++++ | 49% ~45s
|++++++++++++++++++++++++++ | 51% ~44s
|++++++++++++++++++++++++++ | 52% ~42s
|+++++++++++++++++++++++++++ | 53% ~41s
|+++++++++++++++++++++++++++ | 54% ~40s
|++++++++++++++++++++++++++++ | 55% ~39s
|++++++++++++++++++++++++++++ | 56% ~38s
|+++++++++++++++++++++++++++++ | 57% ~37s
|+++++++++++++++++++++++++++++ | 58% ~36s
|++++++++++++++++++++++++++++++ | 59% ~35s
|++++++++++++++++++++++++++++++ | 60% ~34s
|+++++++++++++++++++++++++++++++ | 61% ~34s
|++++++++++++++++++++++++++++++++ | 62% ~33s
|++++++++++++++++++++++++++++++++ | 63% ~32s
|+++++++++++++++++++++++++++++++++ | 64% ~31s
|+++++++++++++++++++++++++++++++++ | 65% ~30s
|++++++++++++++++++++++++++++++++++ | 66% ~29s
|++++++++++++++++++++++++++++++++++ | 67% ~28s
|+++++++++++++++++++++++++++++++++++ | 68% ~27s
|+++++++++++++++++++++++++++++++++++ | 69% ~26s
|++++++++++++++++++++++++++++++++++++ | 71% ~25s
|++++++++++++++++++++++++++++++++++++ | 72% ~24s
|+++++++++++++++++++++++++++++++++++++ | 73% ~23s
|+++++++++++++++++++++++++++++++++++++ | 74% ~22s
|++++++++++++++++++++++++++++++++++++++ | 75% ~21s
|++++++++++++++++++++++++++++++++++++++ | 76% ~21s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~20s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~19s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~18s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~17s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~16s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~15s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~14s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 24s
| | 0 % ~calculating
|+ | 1 % ~01m 45s
|++ | 2 % ~01m 45s
|++ | 3 % ~01m 43s
|+++ | 4 % ~01m 41s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+++ | 5 % ~01m 44s
|++++ | 6 % ~01m 42s
|++++ | 7 % ~01m 43s
|+++++ | 8 % ~01m 41s
|+++++ | 9 % ~01m 43s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++ | 10% ~01m 42s
|++++++ | 11% ~01m 41s
|+++++++ | 12% ~01m 40s
|+++++++ | 13% ~01m 38s
|++++++++ | 14% ~01m 37s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++++ | 15% ~01m 37s
|+++++++++ | 16% ~01m 35s
|+++++++++ | 17% ~01m 34s
|++++++++++ | 18% ~01m 33s
|++++++++++ | 19% ~01m 31s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+++++++++++ | 20% ~01m 31s
|+++++++++++ | 21% ~01m 29s
|++++++++++++ | 22% ~01m 28s
|++++++++++++ | 23% ~01m 27s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+++++++++++++ | 24% ~01m 26s
|+++++++++++++ | 25% ~01m 25s
|++++++++++++++ | 26% ~01m 24s
|++++++++++++++ | 27% ~01m 23s
|+++++++++++++++ | 28% ~01m 21s
|+++++++++++++++ | 29% ~01m 20s
|++++++++++++++++ | 30% ~01m 18s
|++++++++++++++++ | 31% ~01m 17s
|+++++++++++++++++ | 32% ~01m 16s
|+++++++++++++++++ | 33% ~01m 14s
|++++++++++++++++++ | 34% ~01m 13s
|++++++++++++++++++ | 35% ~01m 11s
|+++++++++++++++++++ | 36% ~01m 10s
|+++++++++++++++++++ | 37% ~01m 09s
|++++++++++++++++++++ | 38% ~01m 08s
|++++++++++++++++++++ | 39% ~01m 07s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+++++++++++++++++++++ | 40% ~01m 05s
|+++++++++++++++++++++ | 41% ~01m 04s
|++++++++++++++++++++++ | 42% ~01m 03s
|++++++++++++++++++++++ | 43% ~01m 02s
|+++++++++++++++++++++++ | 44% ~01m 00s
|+++++++++++++++++++++++ | 45% ~59s
|++++++++++++++++++++++++ | 46% ~58s
|++++++++++++++++++++++++ | 47% ~57s
|+++++++++++++++++++++++++ | 48% ~56s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+++++++++++++++++++++++++ | 49% ~55s
|++++++++++++++++++++++++++ | 51% ~54s
|++++++++++++++++++++++++++ | 52% ~52s
|+++++++++++++++++++++++++++ | 53% ~51s
|+++++++++++++++++++++++++++ | 54% ~50s
|++++++++++++++++++++++++++++ | 55% ~49s
|++++++++++++++++++++++++++++ | 56% ~48s
|+++++++++++++++++++++++++++++ | 57% ~47s
|+++++++++++++++++++++++++++++ | 58% ~46s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++++++++++++++++++++++++++ | 59% ~45s
|++++++++++++++++++++++++++++++ | 60% ~44s
|+++++++++++++++++++++++++++++++ | 61% ~43s
|+++++++++++++++++++++++++++++++ | 62% ~42s
|++++++++++++++++++++++++++++++++ | 63% ~41s
|++++++++++++++++++++++++++++++++ | 64% ~39s
|+++++++++++++++++++++++++++++++++ | 65% ~38s
|+++++++++++++++++++++++++++++++++ | 66% ~37s
|++++++++++++++++++++++++++++++++++ | 67% ~36s
|++++++++++++++++++++++++++++++++++ | 68% ~35s
|+++++++++++++++++++++++++++++++++++ | 69% ~34s
|+++++++++++++++++++++++++++++++++++ | 70% ~33s
|++++++++++++++++++++++++++++++++++++ | 71% ~32s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++++++++++++++++++++++++++++++++ | 72% ~31s
|+++++++++++++++++++++++++++++++++++++ | 73% ~29s
|+++++++++++++++++++++++++++++++++++++ | 74% ~28s
|++++++++++++++++++++++++++++++++++++++ | 75% ~27s
|++++++++++++++++++++++++++++++++++++++ | 76% ~26s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~25s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~24s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~23s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~22s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~21s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~20s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~19s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~18s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~17s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~15s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~07s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 48s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
| | 0 % ~calculating
|+ | 1 % ~01m 52s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++ | 2 % ~01m 59s
|++ | 3 % ~01m 54s
|+++ | 4 % ~01m 50s
|+++ | 5 % ~01m 48s
|++++ | 6 % ~01m 45s
|++++ | 7 % ~01m 42s
|+++++ | 8 % ~01m 42s
|+++++ | 9 % ~01m 40s
|++++++ | 10% ~01m 38s
|++++++ | 11% ~01m 36s
|+++++++ | 12% ~01m 34s
|+++++++ | 13% ~01m 32s
|++++++++ | 14% ~01m 31s
|++++++++ | 15% ~01m 30s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+++++++++ | 16% ~01m 30s
|+++++++++ | 18% ~01m 29s
|++++++++++ | 19% ~01m 28s
|++++++++++ | 20% ~01m 27s
|+++++++++++ | 21% ~01m 26s
|+++++++++++ | 22% ~01m 25s
|++++++++++++ | 23% ~01m 24s
|++++++++++++ | 24% ~01m 23s
|+++++++++++++ | 25% ~01m 22s
|+++++++++++++ | 26% ~01m 21s
|++++++++++++++ | 27% ~01m 20s
|++++++++++++++ | 28% ~01m 19s
|+++++++++++++++ | 29% ~01m 18s
|+++++++++++++++ | 30% ~01m 17s
|++++++++++++++++ | 31% ~01m 16s
|++++++++++++++++ | 32% ~01m 15s
|+++++++++++++++++ | 33% ~01m 14s
|++++++++++++++++++ | 34% ~01m 12s
|++++++++++++++++++ | 35% ~01m 11s
|+++++++++++++++++++ | 36% ~01m 10s
|+++++++++++++++++++ | 37% ~01m 09s
|++++++++++++++++++++ | 38% ~01m 07s
|++++++++++++++++++++ | 39% ~01m 06s
|+++++++++++++++++++++ | 40% ~01m 05s
|+++++++++++++++++++++ | 41% ~01m 04s
|++++++++++++++++++++++ | 42% ~01m 03s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++++++++++++++++++ | 43% ~01m 02s
|+++++++++++++++++++++++ | 44% ~01m 01s
|+++++++++++++++++++++++ | 45% ~60s
|++++++++++++++++++++++++ | 46% ~59s
|++++++++++++++++++++++++ | 47% ~57s
|+++++++++++++++++++++++++ | 48% ~56s
|+++++++++++++++++++++++++ | 49% ~55s
|++++++++++++++++++++++++++ | 51% ~54s
|++++++++++++++++++++++++++ | 52% ~53s
|+++++++++++++++++++++++++++ | 53% ~51s
|+++++++++++++++++++++++++++ | 54% ~50s
|++++++++++++++++++++++++++++ | 55% ~49s
|++++++++++++++++++++++++++++ | 56% ~48s
|+++++++++++++++++++++++++++++ | 57% ~47s
|+++++++++++++++++++++++++++++ | 58% ~46s
|++++++++++++++++++++++++++++++ | 59% ~44s
|++++++++++++++++++++++++++++++ | 60% ~43s
|+++++++++++++++++++++++++++++++ | 61% ~42s
|+++++++++++++++++++++++++++++++ | 62% ~41s
|++++++++++++++++++++++++++++++++ | 63% ~40s
|++++++++++++++++++++++++++++++++ | 64% ~39s
|+++++++++++++++++++++++++++++++++ | 65% ~38s
|+++++++++++++++++++++++++++++++++ | 66% ~37s
|++++++++++++++++++++++++++++++++++ | 67% ~36s
|+++++++++++++++++++++++++++++++++++ | 68% ~35s
|+++++++++++++++++++++++++++++++++++ | 69% ~34s
|++++++++++++++++++++++++++++++++++++ | 70% ~33s
|++++++++++++++++++++++++++++++++++++ | 71% ~31s
|+++++++++++++++++++++++++++++++++++++ | 72% ~30s
|+++++++++++++++++++++++++++++++++++++ | 73% ~29s
|++++++++++++++++++++++++++++++++++++++ | 74% ~28s
|++++++++++++++++++++++++++++++++++++++ | 75% ~27s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~26s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~25s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~23s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~22s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~21s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~20s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~19s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~18s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~17s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~16s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~14s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 50s
| | 0 % ~calculating
|+ | 1 % ~01m 24s
|++ | 2 % ~01m 27s
|++ | 3 % ~01m 32s
|+++ | 4 % ~01m 31s
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+++ | 5 % ~01m 33s
|++++ | 6 % ~01m 34s
|++++ | 7 % ~01m 32s
|+++++ | 8 % ~01m 30s
|+++++ | 9 % ~01m 27s
|++++++ | 11% ~01m 26s
|++++++ | 12% ~01m 24s
|+++++++ | 13% ~01m 22s
|+++++++ | 14% ~01m 21s
|++++++++ | 15% ~01m 20s
|++++++++ | 16% ~01m 19s
|+++++++++ | 17% ~01m 18s
|+++++++++ | 18% ~01m 17s
|++++++++++ | 19% ~01m 16s
|++++++++++ | 20% ~01m 16s
|+++++++++++ | 21% ~01m 15s
|++++++++++++ | 22% ~01m 14s
|++++++++++++ | 23% ~01m 14s
|+++++++++++++ | 24% ~01m 14s
|+++++++++++++ | 25% ~01m 13s
|++++++++++++++ | 26% ~01m 12s
|++++++++++++++ | 27% ~01m 12s
|+++++++++++++++ | 28% ~01m 11s
|+++++++++++++++ | 29% ~01m 10s
|++++++++++++++++ | 31% ~01m 09s
|++++++++++++++++ | 32% ~01m 08s
|+++++++++++++++++ | 33% ~01m 07s
|+++++++++++++++++ | 34% ~01m 08s
|++++++++++++++++++ | 35% ~01m 07s
|++++++++++++++++++ | 36% ~01m 06s
|+++++++++++++++++++ | 37% ~01m 05s
|+++++++++++++++++++ | 38% ~01m 04s
|++++++++++++++++++++ | 39% ~01m 03s
|++++++++++++++++++++ | 40% ~01m 02s
|+++++++++++++++++++++ | 41% ~01m 00s
|++++++++++++++++++++++ | 42% ~59s
|++++++++++++++++++++++ | 43% ~58s
|+++++++++++++++++++++++ | 44% ~57s
|+++++++++++++++++++++++ | 45% ~56s
|++++++++++++++++++++++++ | 46% ~55s
|++++++++++++++++++++++++ | 47% ~54s
|+++++++++++++++++++++++++ | 48% ~53s
|+++++++++++++++++++++++++ | 49% ~51s
|++++++++++++++++++++++++++ | 51% ~50s
|++++++++++++++++++++++++++ | 52% ~49s
|+++++++++++++++++++++++++++ | 53% ~48s
|+++++++++++++++++++++++++++ | 54% ~47s
|++++++++++++++++++++++++++++ | 55% ~46s
|++++++++++++++++++++++++++++ | 56% ~45s
|+++++++++++++++++++++++++++++ | 57% ~44s
|+++++++++++++++++++++++++++++ | 58% ~43s
|++++++++++++++++++++++++++++++ | 59% ~42s
|++++++++++++++++++++++++++++++ | 60% ~41s
|+++++++++++++++++++++++++++++++ | 61% ~39s
|++++++++++++++++++++++++++++++++ | 62% ~38s
|++++++++++++++++++++++++++++++++ | 63% ~37s
|+++++++++++++++++++++++++++++++++ | 64% ~36s
|+++++++++++++++++++++++++++++++++ | 65% ~35s
|++++++++++++++++++++++++++++++++++ | 66% ~34s
|++++++++++++++++++++++++++++++++++ | 67% ~33s
|+++++++++++++++++++++++++++++++++++ | 68% ~32s
|+++++++++++++++++++++++++++++++++++ | 69% ~31s
|++++++++++++++++++++++++++++++++++++ | 71% ~30s
|++++++++++++++++++++++++++++++++++++ | 72% ~29s
|+++++++++++++++++++++++++++++++++++++ | 73% ~28s
|+++++++++++++++++++++++++++++++++++++ | 74% ~27s
|++++++++++++++++++++++++++++++++++++++ | 75% ~25s
|++++++++++++++++++++++++++++++++++++++ | 76% ~24s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~23s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~22s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~21s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~20s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~19s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~18s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~17s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~16s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 41s
| | 0 % ~calculating
|+ | 1 % ~01m 11s
|++ | 2 % ~01m 03s
|++ | 3 % ~59s
|+++ | 4 % ~56s
|+++ | 5 % ~55s
|++++ | 6 % ~54s
|++++ | 7 % ~53s
|+++++ | 8 % ~52s
|+++++ | 9 % ~51s
|++++++ | 10% ~50s
|++++++ | 11% ~50s
|+++++++ | 12% ~49s
|+++++++ | 13% ~48s
|++++++++ | 14% ~49s
|++++++++ | 15% ~50s
|+++++++++ | 16% ~49s
|+++++++++ | 17% ~48s
|++++++++++ | 18% ~48s
|++++++++++ | 19% ~48s
|+++++++++++ | 20% ~48s
|+++++++++++ | 21% ~48s
|++++++++++++ | 22% ~48s
|++++++++++++ | 23% ~47s
|+++++++++++++ | 24% ~46s
|+++++++++++++ | 26% ~45s
|++++++++++++++ | 27% ~45s
|++++++++++++++ | 28% ~44s
|+++++++++++++++ | 29% ~43s
|+++++++++++++++ | 30% ~42s
|++++++++++++++++ | 31% ~42s
|++++++++++++++++ | 32% ~41s
|+++++++++++++++++ | 33% ~40s
|+++++++++++++++++ | 34% ~40s
|++++++++++++++++++ | 35% ~40s
|++++++++++++++++++ | 36% ~40s
|+++++++++++++++++++ | 37% ~39s
|+++++++++++++++++++ | 38% ~39s
|++++++++++++++++++++ | 39% ~38s
|++++++++++++++++++++ | 40% ~38s
|+++++++++++++++++++++ | 41% ~38s
|+++++++++++++++++++++ | 42% ~38s
|++++++++++++++++++++++ | 43% ~37s
|++++++++++++++++++++++ | 44% ~36s
|+++++++++++++++++++++++ | 45% ~36s
|+++++++++++++++++++++++ | 46% ~35s
|++++++++++++++++++++++++ | 47% ~34s
|++++++++++++++++++++++++ | 48% ~34s
|+++++++++++++++++++++++++ | 49% ~33s
|+++++++++++++++++++++++++ | 50% ~32s
|++++++++++++++++++++++++++ | 51% ~31s
|+++++++++++++++++++++++++++ | 52% ~31s
|+++++++++++++++++++++++++++ | 53% ~30s
|++++++++++++++++++++++++++++ | 54% ~29s
|++++++++++++++++++++++++++++ | 55% ~29s
|+++++++++++++++++++++++++++++ | 56% ~28s
|+++++++++++++++++++++++++++++ | 57% ~27s
|++++++++++++++++++++++++++++++ | 58% ~27s
|++++++++++++++++++++++++++++++ | 59% ~26s
|+++++++++++++++++++++++++++++++ | 60% ~26s
|+++++++++++++++++++++++++++++++ | 61% ~25s
|++++++++++++++++++++++++++++++++ | 62% ~24s
|++++++++++++++++++++++++++++++++ | 63% ~24s
|+++++++++++++++++++++++++++++++++ | 64% ~23s
|+++++++++++++++++++++++++++++++++ | 65% ~23s
|++++++++++++++++++++++++++++++++++ | 66% ~22s
|++++++++++++++++++++++++++++++++++ | 67% ~21s
|+++++++++++++++++++++++++++++++++++ | 68% ~21s
|+++++++++++++++++++++++++++++++++++ | 69% ~20s
|++++++++++++++++++++++++++++++++++++ | 70% ~19s
|++++++++++++++++++++++++++++++++++++ | 71% ~19s
|+++++++++++++++++++++++++++++++++++++ | 72% ~18s
|+++++++++++++++++++++++++++++++++++++ | 73% ~18s
|++++++++++++++++++++++++++++++++++++++ | 74% ~17s
|++++++++++++++++++++++++++++++++++++++ | 76% ~16s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~16s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~15s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~15s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~14s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~13s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~12s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 10s
| | 0 % ~calculating
|+ | 1 % ~01m 49s
|++ | 2 % ~01m 47s
|++ | 3 % ~01m 45s
|+++ | 4 % ~01m 42s
|+++ | 5 % ~01m 41s
|++++ | 6 % ~01m 41s
|++++ | 7 % ~01m 40s
|+++++ | 8 % ~01m 39s
|+++++ | 9 % ~01m 39s
|++++++ | 10% ~01m 38s
|++++++ | 11% ~01m 37s
|+++++++ | 12% ~01m 38s
|+++++++ | 13% ~01m 37s
|++++++++ | 14% ~01m 35s
|++++++++ | 15% ~01m 34s
|+++++++++ | 16% ~01m 32s
|+++++++++ | 17% ~01m 31s
|++++++++++ | 18% ~01m 30s
|++++++++++ | 19% ~01m 29s
|+++++++++++ | 20% ~01m 28s
|+++++++++++ | 21% ~01m 27s
|++++++++++++ | 22% ~01m 26s
|++++++++++++ | 23% ~01m 25s
|+++++++++++++ | 24% ~01m 25s
|+++++++++++++ | 26% ~01m 24s
|++++++++++++++ | 27% ~01m 22s
|++++++++++++++ | 28% ~01m 21s
|+++++++++++++++ | 29% ~01m 20s
|+++++++++++++++ | 30% ~01m 18s
|++++++++++++++++ | 31% ~01m 17s
|++++++++++++++++ | 32% ~01m 16s
|+++++++++++++++++ | 33% ~01m 15s
|+++++++++++++++++ | 34% ~01m 14s
|++++++++++++++++++ | 35% ~01m 13s
|++++++++++++++++++ | 36% ~01m 12s
|+++++++++++++++++++ | 37% ~01m 11s
|+++++++++++++++++++ | 38% ~01m 10s
|++++++++++++++++++++ | 39% ~01m 09s
|++++++++++++++++++++ | 40% ~01m 08s
|+++++++++++++++++++++ | 41% ~01m 06s
|+++++++++++++++++++++ | 42% ~01m 05s
|++++++++++++++++++++++ | 43% ~01m 04s
|++++++++++++++++++++++ | 44% ~01m 03s
|+++++++++++++++++++++++ | 45% ~01m 02s
|+++++++++++++++++++++++ | 46% ~01m 01s
|++++++++++++++++++++++++ | 47% ~60s
|++++++++++++++++++++++++ | 48% ~59s
|+++++++++++++++++++++++++ | 49% ~58s
|+++++++++++++++++++++++++ | 50% ~57s
|++++++++++++++++++++++++++ | 51% ~56s
|+++++++++++++++++++++++++++ | 52% ~55s
|+++++++++++++++++++++++++++ | 53% ~55s
|++++++++++++++++++++++++++++ | 54% ~54s
|++++++++++++++++++++++++++++ | 55% ~52s
|+++++++++++++++++++++++++++++ | 56% ~51s
|+++++++++++++++++++++++++++++ | 57% ~50s
|++++++++++++++++++++++++++++++ | 58% ~49s
|++++++++++++++++++++++++++++++ | 59% ~48s
|+++++++++++++++++++++++++++++++ | 60% ~46s
|+++++++++++++++++++++++++++++++ | 61% ~45s
|++++++++++++++++++++++++++++++++ | 62% ~44s
|++++++++++++++++++++++++++++++++ | 63% ~43s
|+++++++++++++++++++++++++++++++++ | 64% ~42s
|+++++++++++++++++++++++++++++++++ | 65% ~40s
|++++++++++++++++++++++++++++++++++ | 66% ~39s
|++++++++++++++++++++++++++++++++++ | 67% ~38s
|+++++++++++++++++++++++++++++++++++ | 68% ~37s
|+++++++++++++++++++++++++++++++++++ | 69% ~36s
|++++++++++++++++++++++++++++++++++++ | 70% ~35s
|++++++++++++++++++++++++++++++++++++ | 71% ~34s
|+++++++++++++++++++++++++++++++++++++ | 72% ~32s
|+++++++++++++++++++++++++++++++++++++ | 73% ~31s
|++++++++++++++++++++++++++++++++++++++ | 74% ~30s
|++++++++++++++++++++++++++++++++++++++ | 76% ~29s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~27s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~26s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~25s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~24s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~23s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~22s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~20s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~19s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~18s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~17s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~16s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~14s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~12s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 58s
| | 0 % ~calculating
|+ | 1 % ~03m 04s
|++ | 2 % ~03m 03s
|++ | 3 % ~02m 57s
|+++ | 4 % ~02m 56s
|+++ | 5 % ~02m 55s
|++++ | 6 % ~02m 54s
|++++ | 7 % ~02m 53s
|+++++ | 8 % ~02m 50s
|+++++ | 9 % ~02m 51s
|++++++ | 10% ~02m 48s
|++++++ | 11% ~02m 51s
|+++++++ | 12% ~02m 48s
|+++++++ | 13% ~02m 46s
|++++++++ | 14% ~02m 44s
|++++++++ | 15% ~02m 43s
|+++++++++ | 16% ~02m 42s
|+++++++++ | 18% ~02m 40s
|++++++++++ | 19% ~02m 39s
|++++++++++ | 20% ~02m 37s
|+++++++++++ | 21% ~02m 35s
|+++++++++++ | 22% ~02m 33s
|++++++++++++ | 23% ~02m 30s
|++++++++++++ | 24% ~02m 29s
|+++++++++++++ | 25% ~02m 27s
|+++++++++++++ | 26% ~02m 25s
|++++++++++++++ | 27% ~02m 23s
|++++++++++++++ | 28% ~02m 20s
|+++++++++++++++ | 29% ~02m 18s
|+++++++++++++++ | 30% ~02m 16s
|++++++++++++++++ | 31% ~02m 14s
|++++++++++++++++ | 32% ~02m 12s
|+++++++++++++++++ | 33% ~02m 10s
|++++++++++++++++++ | 34% ~02m 08s
|++++++++++++++++++ | 35% ~02m 06s
|+++++++++++++++++++ | 36% ~02m 04s
|+++++++++++++++++++ | 37% ~02m 03s
|++++++++++++++++++++ | 38% ~02m 01s
|++++++++++++++++++++ | 39% ~01m 60s
|+++++++++++++++++++++ | 40% ~01m 57s
|+++++++++++++++++++++ | 41% ~01m 55s
|++++++++++++++++++++++ | 42% ~01m 53s
|++++++++++++++++++++++ | 43% ~01m 51s
|+++++++++++++++++++++++ | 44% ~01m 49s
|+++++++++++++++++++++++ | 45% ~01m 47s
|++++++++++++++++++++++++ | 46% ~01m 45s
|++++++++++++++++++++++++ | 47% ~01m 43s
|+++++++++++++++++++++++++ | 48% ~01m 41s
|+++++++++++++++++++++++++ | 49% ~01m 39s
|++++++++++++++++++++++++++ | 51% ~01m 37s
|++++++++++++++++++++++++++ | 52% ~01m 35s
|+++++++++++++++++++++++++++ | 53% ~01m 33s
|+++++++++++++++++++++++++++ | 54% ~01m 31s
|++++++++++++++++++++++++++++ | 55% ~01m 30s
|++++++++++++++++++++++++++++ | 56% ~01m 27s
|+++++++++++++++++++++++++++++ | 57% ~01m 25s
|+++++++++++++++++++++++++++++ | 58% ~01m 24s
|++++++++++++++++++++++++++++++ | 59% ~01m 21s
|++++++++++++++++++++++++++++++ | 60% ~01m 19s
|+++++++++++++++++++++++++++++++ | 61% ~01m 17s
|+++++++++++++++++++++++++++++++ | 62% ~01m 15s
|++++++++++++++++++++++++++++++++ | 63% ~01m 13s
|++++++++++++++++++++++++++++++++ | 64% ~01m 11s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 09s
|+++++++++++++++++++++++++++++++++ | 66% ~01m 07s
|++++++++++++++++++++++++++++++++++ | 67% ~01m 05s
|+++++++++++++++++++++++++++++++++++ | 68% ~01m 03s
|+++++++++++++++++++++++++++++++++++ | 69% ~01m 01s
|++++++++++++++++++++++++++++++++++++ | 70% ~59s
|++++++++++++++++++++++++++++++++++++ | 71% ~57s
|+++++++++++++++++++++++++++++++++++++ | 72% ~55s
|+++++++++++++++++++++++++++++++++++++ | 73% ~53s
|++++++++++++++++++++++++++++++++++++++ | 74% ~51s
|++++++++++++++++++++++++++++++++++++++ | 75% ~49s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~47s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~45s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~43s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~41s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~39s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~37s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~35s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~33s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~31s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~29s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~27s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~25s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~23s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~20s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~18s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~16s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~14s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~12s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03m 16s
| | 0 % ~calculating
|+ | 1 % ~02m 36s
|+ | 2 % ~02m 42s
|++ | 3 % ~02m 40s
|++ | 4 % ~02m 35s
|+++ | 5 % ~02m 37s
|+++ | 6 % ~02m 34s
|++++ | 7 % ~02m 29s
|++++ | 8 % ~02m 26s
|+++++ | 9 % ~02m 23s
|+++++ | 10% ~02m 20s
|++++++ | 11% ~02m 18s
|++++++ | 12% ~02m 15s
|+++++++ | 13% ~02m 13s
|+++++++ | 14% ~02m 11s
|++++++++ | 15% ~02m 09s
|++++++++ | 16% ~02m 07s
|+++++++++ | 17% ~02m 05s
|+++++++++ | 18% ~02m 03s
|++++++++++ | 19% ~02m 02s
|++++++++++ | 20% ~02m 01s
|+++++++++++ | 21% ~02m 01s
|+++++++++++ | 22% ~01m 59s
|++++++++++++ | 23% ~01m 57s
|++++++++++++ | 24% ~01m 56s
|+++++++++++++ | 25% ~01m 55s
|+++++++++++++ | 26% ~01m 53s
|++++++++++++++ | 27% ~01m 52s
|++++++++++++++ | 28% ~01m 50s
|+++++++++++++++ | 29% ~01m 49s
|+++++++++++++++ | 30% ~01m 47s
|++++++++++++++++ | 31% ~01m 45s
|++++++++++++++++ | 32% ~01m 44s
|+++++++++++++++++ | 33% ~01m 42s
|+++++++++++++++++ | 34% ~01m 40s
|++++++++++++++++++ | 35% ~01m 39s
|++++++++++++++++++ | 36% ~01m 37s
|+++++++++++++++++++ | 37% ~01m 36s
|+++++++++++++++++++ | 38% ~01m 34s
|++++++++++++++++++++ | 39% ~01m 32s
|++++++++++++++++++++ | 40% ~01m 31s
|+++++++++++++++++++++ | 41% ~01m 29s
|+++++++++++++++++++++ | 42% ~01m 28s
|++++++++++++++++++++++ | 43% ~01m 26s
|++++++++++++++++++++++ | 44% ~01m 25s
|+++++++++++++++++++++++ | 45% ~01m 23s
|+++++++++++++++++++++++ | 46% ~01m 23s
|++++++++++++++++++++++++ | 47% ~01m 21s
|++++++++++++++++++++++++ | 48% ~01m 19s
|+++++++++++++++++++++++++ | 49% ~01m 18s
|+++++++++++++++++++++++++ | 50% ~01m 16s
|++++++++++++++++++++++++++ | 51% ~01m 14s
|++++++++++++++++++++++++++ | 52% ~01m 13s
|+++++++++++++++++++++++++++ | 53% ~01m 11s
|+++++++++++++++++++++++++++ | 54% ~01m 10s
|++++++++++++++++++++++++++++ | 55% ~01m 08s
|++++++++++++++++++++++++++++ | 56% ~01m 07s
|+++++++++++++++++++++++++++++ | 57% ~01m 05s
|+++++++++++++++++++++++++++++ | 58% ~01m 03s
|++++++++++++++++++++++++++++++ | 59% ~01m 02s
|++++++++++++++++++++++++++++++ | 60% ~01m 00s
|+++++++++++++++++++++++++++++++ | 61% ~59s
|+++++++++++++++++++++++++++++++ | 62% ~57s
|++++++++++++++++++++++++++++++++ | 63% ~56s
|++++++++++++++++++++++++++++++++ | 64% ~54s
|+++++++++++++++++++++++++++++++++ | 65% ~53s
|+++++++++++++++++++++++++++++++++ | 66% ~51s
|++++++++++++++++++++++++++++++++++ | 67% ~50s
|++++++++++++++++++++++++++++++++++ | 68% ~49s
|+++++++++++++++++++++++++++++++++++ | 69% ~47s
|+++++++++++++++++++++++++++++++++++ | 70% ~46s
|++++++++++++++++++++++++++++++++++++ | 71% ~44s
|++++++++++++++++++++++++++++++++++++ | 72% ~43s
|+++++++++++++++++++++++++++++++++++++ | 73% ~41s
|+++++++++++++++++++++++++++++++++++++ | 74% ~40s
|++++++++++++++++++++++++++++++++++++++ | 75% ~38s
Display the top markers you computed above.
tiss.markers %>% group_by(cluster) %>% top_n(10, avg_diff)
At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:
# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")
# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
annotation <- c("epithelial cell of large intestine", "enterocyte of epithelium of large intestine", "epithelial cell of large intestine", "large intestine goblet cell", "large intestine goblet cell", "epithelial cell of large intestine", "enterocyte of epithelium of large intestine", "large intestine goblet cell", "large intestine goblet cell", "epithelial cell of large intestine", "large intestine goblet cell", "Brush cell of epithelium proper of large intestine", "enteroendocrine cell")
cell_ontology_id <- c("CL:0002253", "CL:0002071", "CL:0002253", "CL:1000320", "CL:1000320", "CL:0002253", "CL:0002071", "CL:1000320", "CL:1000320", "CL:0002253", "CL:1000320", "CL:0002203", "CL:0000164")
tiss@meta.data[,'annotation'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = annotation)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)
tiss@meta.data[tiss@cell.names,'annotation'] <- as.character(tiss@meta.data$annotation)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)
TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='annotation')
Color by metadata, like plate barcode, to check for batch effects.
TSNEPlot(object = tiss, do.return = TRUE, group.by = "plate.barcode")
Print a table showing the count of cells in each identity category from each plate.
table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))
MAA000583 MAA000611 MAA000612 MAA000870 MAA000871 MAA000909 MAA000937 MAA001539 MAA001632 MAA001869 MAA001871 MAA001872
0 11 113 89 49 167 173 9 23 55 30 100 104
1 0 80 73 1 75 89 8 0 86 86 80 82
10 0 0 0 0 1 0 0 2 1 109 7 0
11 0 1 3 2 11 5 3 0 2 6 9 10
12 0 4 1 4 4 3 1 4 2 2 0 0
2 6 90 102 9 37 20 23 3 131 13 68 73
3 0 22 20 4 30 12 5 1 51 19 23 31
4 72 0 0 64 0 0 74 0 0 0 0 0
5 59 0 1 47 1 0 70 2 0 0 0 0
6 65 0 0 32 1 0 26 20 0 0 0 0
7 1 0 0 1 0 0 0 8 0 0 0 0
8 12 1 0 30 2 1 11 61 1 1 1 0
9 0 1 0 9 0 0 1 106 0 0 0 3
MAA001873 MAA001875
0 37 29
1 2 0
10 12 0
11 1 2
12 0 0
2 0 12
3 9 4
4 0 12
5 1 35
6 20 36
7 145 1
8 10 9
9 4 8
When you save the annotated tissue, please give it a name.
filename = here('00_data_ingest', 'tissue_seurat_robj',
paste0(tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
save(tiss, file=filename)
So that Biohub can easily combine all your annotations, please export them as a simple csv.
head(tiss@meta.data)
filename = here('00_data_ingest', 'tissue_annotation_csv',
paste0(tissue_of_interest, "_annotation.csv"))
write.csv(tiss@meta.data[,c('plate.barcode','annotation','cell_ontology_id')], file=filename)